grl

Generic Reinforcement Learning Library

Copyright 2015-2022 Wouter Caarls

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Introduction

GRL is a C++ reinforcement learning library that aims to easily allow evaluating different algorithms through a declarative configuration interface.

Installation

Ubuntu 22.04

sudo apt install git cmake g++ libeigen3-dev libpython3-dev python3-distutils libz-dev
git clone https://github.com/wcaarls/grl.git

For the visualization, additionally install

sudo apt-get install libgl1-mesa-dev freeglut3-dev

For the configurator, additionally install

sudo apt-get install python3-yaml python3-tk

For the tensorflow addon, additionall install

sudo apt-get install libprotobuf-dev protobuf-compiler

# Tensorflow C API 2.8.0
wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.8.0.tar.gz
sudo tar zxvf libtensorflow-gpu-linux-x86_64-2.8.0.tar.gz -C /usr/local

# CUDA Toolkit 11.7
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"
sudo apt-get update
sudo apt-get install cuda

### cuDNN 8.2.1
wget https://anaconda.org/anaconda/cudnn/8.2.1/download/linux-64/cudnn-8.2.1-cuda11.3_0.tar.bz2
sudo tar -jxvf cudnn-8.2.1-cuda11.3_0.tar.bz2 -C /usr/local --wildcards "lib/*"

Then edit ~/.bashrc to include the following, and open a new terminal

export PATH=$PATH:/usr/local/cuda-11/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda-11/lib64

Install Tensorflow 2 for Python

sudo -H python3 -m pip install tensorflow

If there are errors relating to Python loading libtensorflow_framework.so.2, it is because it comes with its own version, and it finds the version from the C API. Workaround:

sudo apt-get install patchelf
sudo patchelf --replace-needed libtensorflow_framework.so.2 libtensorflow_framework.so.2.8.0 /usr/local/lib/libtensorflow.so.2.8.0
sudo rm /usr/local/lib/libtensorflow_framework.so /usr/local/lib/libtensorflow_framework.so.2

Setup

mkdir build
cd build
cmake ..
make

Running

To directly perform an experiment, run

./grld ../cfg/pendulum/sarsa_tc.yaml

To start the configurator instead, run

cd ../bin
./grlc ../cfg/pendulum/sarsa_tc.yaml

Visualizations

GRL comes with standard visualizations for value functions, policies, and the integrated environments (e.g. pendulum swing-up, cart-pole swing-up, compass walker)

Further reading

See grl.pdf